A vision for machine learning in gravitational-wave data analysis

Recent advances in machine learning for gravitational wave physics have fostered new tools to enhance low-latency detection, noise reduction, and rapid data dissemination. There are now platforms focused on gravitational waves offering standardized, scalable infrastructure to streamline machine learning model training and deployment for gravitational-wave detection, optimizing heterogeneous computing environments for efficient, real-time performance. Complementing this, coherence monitoring and denoising address noise reduction from various sources—such as environmental and instrumental couplings—using convolutional neural networks to improve detector sensitivity across frequency bands. For unmodeled gravitational-wave signals, fast, semi-supervised anomaly detection pipelines now extend possibilities for detection beyond template-based searches, enabling low-latency detection of uncharacterized transients. These improvements come at a time when the LIGO-Virgo-KAGRA collaboration's alert system facilitates rapid dissemination of gravitational-wave detections within seconds, enabling near real-time multi-messenger observations and laying groundwork for future low-latency enhancements. I will present a vision for a future machine-learning informed alert system enabled by these developments, pushing the limit of real time searches and alerts enabling multi-messenger astronomy.